Propensity Score Matching Table

In the statistical analysis of observational data propensity score matching psm is a statistical matching technique that attempts to estimate the effect of a treatment policy or other intervention by accounting for the covariates that predict receiving the treatment.

Propensity score matching table.

A quick example of using psmatch2 to implement propensity score matching in stata. A one parameter power function fits the cdf of the gps vector and a resulting scalar balancing score is used for matching and or stratification. So conveniently the r matchit propensity score matching package comes with a subset of the lalonde data set referenced in mhe. Based on descriptives it looks like this data matches columns 1 and 4 in table 3 3 2.

The teffects psmatch command has one very important. According to wikipedia propensity score matching psm is a statistical matching technique that attempts to estimate the effect of a treatment policy or other intervention by accounting for the covariates that predict receiving the treatment in a broader sense propensity score analysis assumes that an unbiased comparison between samples can only be made when the subjects of both. Propensity score matching in stata using teffects. Comparable but patients with the same propensity score are comparable.

For many years the standard tool for propensity score matching in stata has been the psmatch2 command written by edwin leuven and barbara sianesi. In general the propensity score methods give similar results to the logistic regression model. Note the slight discrepancy in statistical significance for the matching method where the 95 confidence interval for the odds ratio was calculated by the standard approximation and may be too wide. For example if a patient with a 70 propensity score underwent the ross procedure and another with a 70 propensity score received a mechanical valve then in theory any difference in outcome can be attributed to the treatment rather than to patient selection.

Specifically the generalized propensity score cumulative distribution function gps cdf method is introduced. This is well known finding from previous empirical and simulation studies. 1 select covariates 2 assess table 1 balance in risk factors before propensity score implementation 3 estimate and implement the propensity score in the study cohort 4 reassess table 1 balance in risk factors after.